{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "56132c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1107, 2048)\n",
      "(1107, 2048)\n",
      "拆分后：\n",
      "datax_split.shape: (4428, 512)\n",
      "datay_split.shape: (4428, 512)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Load the reshaped data from the .npy file\n",
    "datax_1 = np.load(r'E:\\14python\\07毫米波雷达心电心音\\datax_train.npy')\n",
    "datax_2 = np.load(r'E:\\14python\\07毫米波雷达心电心音\\datax_test.npy')\n",
    "datay_1 = np.load(r'E:\\14python\\07毫米波雷达心电心音\\datay_train.npy')\n",
    "datay_2 = np.load(r'E:\\14python\\07毫米波雷达心电心音\\datay_test.npy')\n",
    "\n",
    "\n",
    "datax = np.concatenate((datax_1, datax_2), axis=0)\n",
    "datay = np.concatenate((datay_1, datay_2), axis=0)\n",
    "datay = datay.reshape(1107, 2048)\n",
    "print(datax.shape)\n",
    "print(datay.shape)\n",
    "\n",
    "segment_length = datax.shape[1] // 4  # 512\n",
    "\n",
    "# 拆分成 4 段，每段长 512，然后 reshape 成 (4428, 512)\n",
    "datax_split = datax.reshape(-1, 4, segment_length).reshape(-1, segment_length)\n",
    "datay_split = datay.reshape(-1, 4, segment_length).reshape(-1, segment_length)\n",
    "\n",
    "print(\"拆分后：\")\n",
    "print(\"datax_split.shape:\", datax_split.shape)  # (4428, 512)\n",
    "print(\"datay_split.shape:\", datay_split.shape)  # (4428, 512)\n",
    "datax = datax_split\n",
    "datay = datay_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6cd2b4db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Einstein\\AppData\\Local\\Temp\\ipykernel_18876\\2729801934.py:105: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(save_path, map_location=device))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Loaded existing model weights from 'TCN_SIMPLE_best_model.pt'\n",
      "Epoch 1/100 | Train Loss: 0.012999 | Val Loss: 0.008807\n",
      "  ✅ New best model saved at 'TCN_SIMPLE_best_model.pt' with val loss: 0.008807\n",
      "Epoch 2/100 | Train Loss: 0.012925 | Val Loss: 0.008841\n",
      "Epoch 3/100 | Train Loss: 0.012894 | Val Loss: 0.008827\n",
      "Epoch 4/100 | Train Loss: 0.012882 | Val Loss: 0.008875\n",
      "Epoch 5/100 | Train Loss: 0.012893 | Val Loss: 0.008885\n",
      "Epoch 6/100 | Train Loss: 0.012843 | Val Loss: 0.008884\n",
      "Epoch 7/100 | Train Loss: 0.012844 | Val Loss: 0.008946\n",
      "Epoch 8/100 | Train Loss: 0.012831 | Val Loss: 0.008939\n",
      "Epoch 9/100 | Train Loss: 0.012796 | Val Loss: 0.008941\n",
      "Epoch 10/100 | Train Loss: 0.012807 | Val Loss: 0.008970\n",
      "Epoch 11/100 | Train Loss: 0.012788 | Val Loss: 0.008945\n",
      "Epoch 12/100 | Train Loss: 0.012777 | Val Loss: 0.008948\n",
      "Epoch 13/100 | Train Loss: 0.012759 | Val Loss: 0.008969\n",
      "Epoch 14/100 | Train Loss: 0.012748 | Val Loss: 0.008975\n",
      "Epoch 15/100 | Train Loss: 0.012726 | Val Loss: 0.008947\n",
      "Epoch 16/100 | Train Loss: 0.012719 | Val Loss: 0.008971\n",
      "Epoch 17/100 | Train Loss: 0.012739 | Val Loss: 0.008989\n",
      "Epoch 18/100 | Train Loss: 0.012706 | Val Loss: 0.009017\n",
      "Epoch 19/100 | Train Loss: 0.012738 | Val Loss: 0.009023\n",
      "Epoch 20/100 | Train Loss: 0.012720 | Val Loss: 0.009063\n",
      "Epoch 21/100 | Train Loss: 0.012705 | Val Loss: 0.009051\n",
      "Epoch 22/100 | Train Loss: 0.012709 | Val Loss: 0.009081\n",
      "Epoch 23/100 | Train Loss: 0.012665 | Val Loss: 0.009095\n",
      "Epoch 24/100 | Train Loss: 0.012672 | Val Loss: 0.009094\n",
      "Epoch 25/100 | Train Loss: 0.012648 | Val Loss: 0.009034\n",
      "Epoch 26/100 | Train Loss: 0.012641 | Val Loss: 0.009101\n",
      "Epoch 27/100 | Train Loss: 0.012654 | Val Loss: 0.009064\n",
      "Epoch 28/100 | Train Loss: 0.012659 | Val Loss: 0.009171\n",
      "Epoch 29/100 | Train Loss: 0.012636 | Val Loss: 0.009171\n",
      "Epoch 30/100 | Train Loss: 0.012630 | Val Loss: 0.009108\n",
      "Epoch 31/100 | Train Loss: 0.012620 | Val Loss: 0.009159\n",
      "Epoch 32/100 | Train Loss: 0.012608 | Val Loss: 0.009123\n",
      "Epoch 33/100 | Train Loss: 0.012598 | Val Loss: 0.009127\n",
      "Epoch 34/100 | Train Loss: 0.012603 | Val Loss: 0.009127\n",
      "Epoch 35/100 | Train Loss: 0.012578 | Val Loss: 0.009161\n",
      "Epoch 36/100 | Train Loss: 0.012582 | Val Loss: 0.009165\n",
      "Epoch 37/100 | Train Loss: 0.012571 | Val Loss: 0.009129\n",
      "Epoch 38/100 | Train Loss: 0.012559 | Val Loss: 0.009201\n",
      "Epoch 39/100 | Train Loss: 0.012570 | Val Loss: 0.009174\n",
      "Epoch 40/100 | Train Loss: 0.012546 | Val Loss: 0.009149\n",
      "Epoch 41/100 | Train Loss: 0.012561 | Val Loss: 0.009244\n",
      "Epoch 42/100 | Train Loss: 0.012534 | Val Loss: 0.009198\n",
      "Epoch 43/100 | Train Loss: 0.012545 | Val Loss: 0.009222\n",
      "Epoch 44/100 | Train Loss: 0.012534 | Val Loss: 0.009200\n",
      "Epoch 45/100 | Train Loss: 0.012522 | Val Loss: 0.009154\n",
      "Epoch 46/100 | Train Loss: 0.012505 | Val Loss: 0.009213\n",
      "Epoch 47/100 | Train Loss: 0.012504 | Val Loss: 0.009207\n",
      "Epoch 48/100 | Train Loss: 0.012496 | Val Loss: 0.009239\n",
      "Epoch 49/100 | Train Loss: 0.012499 | Val Loss: 0.009249\n",
      "Epoch 50/100 | Train Loss: 0.012490 | Val Loss: 0.009240\n",
      "Epoch 51/100 | Train Loss: 0.012492 | Val Loss: 0.009287\n",
      "Epoch 52/100 | Train Loss: 0.012486 | Val Loss: 0.009231\n",
      "Epoch 53/100 | Train Loss: 0.012482 | Val Loss: 0.009279\n",
      "Epoch 54/100 | Train Loss: 0.012460 | Val Loss: 0.009268\n",
      "Epoch 55/100 | Train Loss: 0.012478 | Val Loss: 0.009251\n",
      "Epoch 56/100 | Train Loss: 0.012450 | Val Loss: 0.009286\n",
      "Epoch 57/100 | Train Loss: 0.012436 | Val Loss: 0.009319\n",
      "Epoch 58/100 | Train Loss: 0.012443 | Val Loss: 0.009292\n",
      "Epoch 59/100 | Train Loss: 0.012422 | Val Loss: 0.009297\n",
      "Epoch 60/100 | Train Loss: 0.012425 | Val Loss: 0.009311\n",
      "Epoch 61/100 | Train Loss: 0.012408 | Val Loss: 0.009315\n",
      "Epoch 62/100 | Train Loss: 0.012428 | Val Loss: 0.009283\n",
      "Epoch 63/100 | Train Loss: 0.012430 | Val Loss: 0.009295\n",
      "Epoch 64/100 | Train Loss: 0.012405 | Val Loss: 0.009319\n",
      "Epoch 65/100 | Train Loss: 0.012418 | Val Loss: 0.009294\n",
      "Epoch 66/100 | Train Loss: 0.012400 | Val Loss: 0.009300\n",
      "Epoch 67/100 | Train Loss: 0.012390 | Val Loss: 0.009340\n",
      "Epoch 68/100 | Train Loss: 0.012393 | Val Loss: 0.009256\n",
      "Epoch 69/100 | Train Loss: 0.012395 | Val Loss: 0.009316\n",
      "Epoch 70/100 | Train Loss: 0.012391 | Val Loss: 0.009304\n",
      "Epoch 71/100 | Train Loss: 0.012360 | Val Loss: 0.009340\n",
      "Epoch 72/100 | Train Loss: 0.012361 | Val Loss: 0.009315\n",
      "Epoch 73/100 | Train Loss: 0.012357 | Val Loss: 0.009348\n",
      "Epoch 74/100 | Train Loss: 0.012363 | Val Loss: 0.009344\n",
      "Epoch 75/100 | Train Loss: 0.012342 | Val Loss: 0.009370\n",
      "Epoch 76/100 | Train Loss: 0.012349 | Val Loss: 0.009376\n",
      "Epoch 77/100 | Train Loss: 0.012322 | Val Loss: 0.009360\n",
      "Epoch 78/100 | Train Loss: 0.012340 | Val Loss: 0.009376\n",
      "Epoch 79/100 | Train Loss: 0.012340 | Val Loss: 0.009378\n",
      "Epoch 80/100 | Train Loss: 0.012329 | Val Loss: 0.009377\n",
      "Epoch 81/100 | Train Loss: 0.012299 | Val Loss: 0.009363\n",
      "Epoch 82/100 | Train Loss: 0.012326 | Val Loss: 0.009376\n",
      "Epoch 83/100 | Train Loss: 0.012314 | Val Loss: 0.009343\n",
      "Epoch 84/100 | Train Loss: 0.012347 | Val Loss: 0.009398\n",
      "Epoch 85/100 | Train Loss: 0.012301 | Val Loss: 0.009376\n",
      "Epoch 86/100 | Train Loss: 0.012315 | Val Loss: 0.009402\n",
      "Epoch 87/100 | Train Loss: 0.012291 | Val Loss: 0.009333\n",
      "Epoch 88/100 | Train Loss: 0.012279 | Val Loss: 0.009372\n",
      "Epoch 89/100 | Train Loss: 0.012299 | Val Loss: 0.009444\n",
      "Epoch 90/100 | Train Loss: 0.012280 | Val Loss: 0.009364\n",
      "Epoch 91/100 | Train Loss: 0.012276 | Val Loss: 0.009404\n",
      "Epoch 92/100 | Train Loss: 0.012248 | Val Loss: 0.009428\n",
      "Epoch 93/100 | Train Loss: 0.012265 | Val Loss: 0.009403\n",
      "Epoch 94/100 | Train Loss: 0.012245 | Val Loss: 0.009433\n",
      "Epoch 95/100 | Train Loss: 0.012260 | Val Loss: 0.009411\n",
      "Epoch 96/100 | Train Loss: 0.012273 | Val Loss: 0.009434\n",
      "Epoch 97/100 | Train Loss: 0.012245 | Val Loss: 0.009399\n",
      "Epoch 98/100 | Train Loss: 0.012236 | Val Loss: 0.009363\n",
      "Epoch 99/100 | Train Loss: 0.012246 | Val Loss: 0.009403\n",
      "Epoch 100/100 | Train Loss: 0.012211 | Val Loss: 0.009404\n",
      "✅ Losses saved to training_losses_20250707_063058.csv\n",
      "✅ Training complete.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Einstein\\AppData\\Local\\Temp\\ipykernel_18876\\2729801934.py:186: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  best_model.load_state_dict(torch.load('TCN_SIMPLE_best_model.pt', map_location=device))\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# ---------- 模型 ----------\n",
    "class Seq2SeqModel(nn.Module):\n",
    "    def __init__(self, input_dim=1, conv_channels=64, conv_kernel_size=7, conv_layers=3,\n",
    "                 gru_hidden=128, gru_layers=2, transformer_heads=4, transformer_layers=4,\n",
    "                 output_dim=1, dropout=0.1):\n",
    "        super().__init__()\n",
    "\n",
    "        # 普通卷积（带 dilation）\n",
    "        convs = []\n",
    "        for i in range(conv_layers):\n",
    "            in_ch = input_dim if i == 0 else conv_channels\n",
    "            dilation = 2 ** i\n",
    "            padding = ((conv_kernel_size - 1) * dilation) // 2\n",
    "            convs.append(nn.Conv1d(in_ch, conv_channels, conv_kernel_size,\n",
    "                                   dilation=dilation, padding=padding))\n",
    "            convs.append(nn.ReLU())\n",
    "        self.conv_layers = nn.Sequential(*convs)\n",
    "\n",
    "        # GRU\n",
    "        self.gru = nn.GRU(input_size=conv_channels,\n",
    "                          hidden_size=gru_hidden,\n",
    "                          num_layers=gru_layers,\n",
    "                          batch_first=True)\n",
    "\n",
    "        # Transformer Encoder\n",
    "        encoder_layer = nn.TransformerEncoderLayer(\n",
    "            d_model=gru_hidden,\n",
    "            nhead=transformer_heads,\n",
    "            dim_feedforward=gru_hidden*4,\n",
    "            dropout=dropout,\n",
    "            activation='relu',\n",
    "            batch_first=True)\n",
    "        self.transformer_encoder = nn.TransformerEncoder(encoder_layer,\n",
    "                                                         num_layers=transformer_layers)\n",
    "\n",
    "        # 输出线性层\n",
    "        self.output_layer = nn.Linear(gru_hidden, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (B, L, 1)\n",
    "        x = x.permute(0, 2, 1)  # (B, 1, L)\n",
    "        x = self.conv_layers(x)  # (B, conv_channels, L)\n",
    "        x = x.permute(0, 2, 1)   # (B, L, conv_channels)\n",
    "        gru_out, _ = self.gru(x)\n",
    "        transformer_out = self.transformer_encoder(gru_out)\n",
    "        out = self.output_layer(transformer_out)  # (B, L, 1)\n",
    "        return out.squeeze(-1)  # (B, L)\n",
    "\n",
    "# ---------- 自定义残差 + 差分损失函数 ----------\n",
    "def residual_diff_loss(input, output, target, diff_weight=0.5):\n",
    "    \"\"\"残差差异损失\"\"\"\n",
    "    pred_seq = input + output\n",
    "    # 重建损失\n",
    "    recon_loss = F.mse_loss(pred_seq, target)\n",
    "    \n",
    "    # 一阶差分损失\n",
    "    diff1 = pred_seq[:, 1:] - pred_seq[:, :-1]\n",
    "    target_diff1 = target[:, 1:] - target[:, :-1]\n",
    "    diff_loss1 = F.mse_loss(diff1, target_diff1)\n",
    "    \n",
    "    # 二阶差分损失\n",
    "    diff2 = pred_seq[:, 2:] - 2 * pred_seq[:, 1:-1] + pred_seq[:, :-2]\n",
    "    target_diff2 = target[:, 2:] - 2 * target[:, 1:-1] + target[:, :-2]\n",
    "    diff_loss2 = F.mse_loss(diff2, target_diff2)\n",
    "    \n",
    "    return recon_loss + diff_weight * (diff_loss1 + diff_loss2)\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
    "import torch.optim as optim\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from pathlib import Path\n",
    "\n",
    "# ---------- 训练函数 ----------\n",
    "\n",
    "def train_model_with_best_save(input_tensor, target_tensor, epochs=100, batch_size=32,\n",
    "                               lr=1e-3, val_ratio=0.1, save_path='best_model.pt',\n",
    "                               diff_weight=1.0, load_best=True):\n",
    "\n",
    "    dataset = TensorDataset(input_tensor, target_tensor)\n",
    "    val_size = int(len(dataset) * val_ratio)\n",
    "    train_size = len(dataset) - val_size\n",
    "    train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=batch_size)\n",
    "\n",
    "    model = Seq2SeqModel(input_dim=1, output_dim=1).to(device)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "    # ========== 加载已有模型权重（如存在） ========== \n",
    "    best_val_loss = float('inf')\n",
    "    if load_best and Path(save_path).exists():\n",
    "        model.load_state_dict(torch.load(save_path, map_location=device))\n",
    "        print(f\"✅ Loaded existing model weights from '{save_path}'\")\n",
    "\n",
    "    # 记录训练和验证损失的列表\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "\n",
    "    # ========== 训练循环 ========== \n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        total_train_loss = 0.0\n",
    "\n",
    "        for batch_x, batch_y in train_loader:\n",
    "            batch_x = batch_x.to(device)\n",
    "            batch_y = batch_y.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            residual = model(batch_x.unsqueeze(-1))  # [B, L, 1] -> (B, L)\n",
    "            loss = residual_diff_loss(batch_x, residual, batch_y, diff_weight)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            total_train_loss += loss.item() * batch_x.size(0)\n",
    "\n",
    "        avg_train_loss = total_train_loss / train_size\n",
    "\n",
    "        # ========== 验证 ========== \n",
    "        model.eval()\n",
    "        total_val_loss = 0.0\n",
    "        with torch.no_grad():\n",
    "            for val_x, val_y in val_loader:\n",
    "                val_x = val_x.to(device)\n",
    "                val_y = val_y.to(device)\n",
    "                residual = model(val_x.unsqueeze(-1))\n",
    "                val_loss = residual_diff_loss(val_x, residual, val_y, diff_weight)\n",
    "                total_val_loss += val_loss.item() * val_x.size(0)\n",
    "\n",
    "        avg_val_loss = total_val_loss / val_size\n",
    "\n",
    "        # 记录每个epoch的损失\n",
    "        train_losses.append(avg_train_loss)\n",
    "        val_losses.append(avg_val_loss)\n",
    "\n",
    "        print(f\"Epoch {epoch+1}/{epochs} | Train Loss: {avg_train_loss:.6f} | Val Loss: {avg_val_loss:.6f}\")\n",
    "\n",
    "        # 保存最佳模型\n",
    "        if avg_val_loss < best_val_loss:\n",
    "            best_val_loss = avg_val_loss\n",
    "            torch.save(model.state_dict(), save_path)\n",
    "            print(f\"  ✅ New best model saved at '{save_path}' with val loss: {best_val_loss:.6f}\")\n",
    "\n",
    "    # 保存损失到 CSV 文件\n",
    "    current_time = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "    csv_filename = f\"training_losses_{current_time}.csv\"\n",
    "    loss_df = pd.DataFrame({\n",
    "        \"Epoch\": range(1, epochs + 1),\n",
    "        \"Train Loss\": train_losses,\n",
    "        \"Val Loss\": val_losses\n",
    "    })\n",
    "    loss_df.to_csv(csv_filename, index=False)\n",
    "    print(f\"✅ Losses saved to {csv_filename}\")\n",
    "\n",
    "    print(\"✅ Training complete.\")\n",
    "    return model\n",
    "\n",
    "# ---------- 主函数测试 ----------\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    input_tensor = torch.tensor(datax, dtype=torch.float32).to(device)\n",
    "    target_tensor = torch.tensor(datay, dtype=torch.float32).to(device)\n",
    "\n",
    "    # 训练模型\n",
    "    model = train_model_with_best_save(input_tensor, target_tensor,\n",
    "                                       epochs=100,\n",
    "                                       batch_size=128,\n",
    "                                       lr=1e-5,\n",
    "                                       save_path='TCN_SIMPLE_best_model.pt',\n",
    "                                       diff_weight=1.0)\n",
    "\n",
    "    # 加载最优模型\n",
    "    best_model = Seq2SeqModel(input_dim=1).to(device)\n",
    "    best_model.load_state_dict(torch.load('TCN_SIMPLE_best_model.pt', map_location=device))\n",
    "    best_model.eval()\n",
    "\n",
    "    # 推理与可视化\n",
    "    with torch.no_grad():\n",
    "        idx = 1000  # 测试第0个样本\n",
    "        test_input = input_tensor[idx].unsqueeze(0).unsqueeze(-1)  # (1, L, 1)\n",
    "        residual = best_model(test_input)  # (1, L)\n",
    "        pred = residual + test_input.squeeze(-1)  # (1, L)\n",
    "\n",
    "        plt.figure(figsize=(12, 5))\n",
    "        plt.plot(pred.squeeze().cpu().numpy(), label='Predicted + Residual')\n",
    "        plt.plot(target_tensor[idx].cpu().numpy(), label='Target', alpha=0.7)\n",
    "        plt.title('Prediction vs Target')\n",
    "        plt.legend()\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    # 可视化训练后的数据\n",
    "    plt.figure()\n",
    "    plt.plot(datax[0])\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(pred.cpu().numpy().flatten())\n",
    "    plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mycv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
